CN111076376A - Method and system for predicting cold load demand and distributing ice storage air conditioner load - Google Patents

Method and system for predicting cold load demand and distributing ice storage air conditioner load Download PDF

Info

Publication number
CN111076376A
CN111076376A CN201911309184.9A CN201911309184A CN111076376A CN 111076376 A CN111076376 A CN 111076376A CN 201911309184 A CN201911309184 A CN 201911309184A CN 111076376 A CN111076376 A CN 111076376A
Authority
CN
China
Prior art keywords
cold
load
ice
ice storage
energy consumption
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201911309184.9A
Other languages
Chinese (zh)
Other versions
CN111076376B (en
Inventor
于军琪
任延欢
赵安军
井文强
丁希生
冉彤
周昕玮
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xian University of Architecture and Technology
Original Assignee
Xian University of Architecture and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xian University of Architecture and Technology filed Critical Xian University of Architecture and Technology
Priority to CN201911309184.9A priority Critical patent/CN111076376B/en
Publication of CN111076376A publication Critical patent/CN111076376A/en
Application granted granted Critical
Publication of CN111076376B publication Critical patent/CN111076376B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/64—Electronic processing using pre-stored data
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/50—Control or safety arrangements characterised by user interfaces or communication
    • F24F11/61—Control or safety arrangements characterised by user interfaces or communication using timers
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F5/00—Air-conditioning systems or apparatus not covered by F24F1/00 or F24F3/00, e.g. using solar heat or combined with household units such as an oven or water heater
    • F24F5/0007—Air-conditioning systems or apparatus not covered by F24F1/00 or F24F3/00, e.g. using solar heat or combined with household units such as an oven or water heater cooling apparatus specially adapted for use in air-conditioning
    • F24F5/0017—Air-conditioning systems or apparatus not covered by F24F1/00 or F24F3/00, e.g. using solar heat or combined with household units such as an oven or water heater cooling apparatus specially adapted for use in air-conditioning using cold storage bodies, e.g. ice
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/14—Thermal energy storage

Abstract

The invention belongs to the field of air conditioner refrigeration, and discloses a cold load demand prediction method and system method and an ice storage air conditioner load distribution method and system. The method is based on the mathematical model of the ice storage air-conditioning system under the requirement of ensuring the comfort of the end user of the target building, takes the three aims of minimum system operation energy consumption, minimum operation cost and minimum energy consumption loss, optimizes the system based on the differential evolution improved particle group combination algorithm of the dispersion control structure, and controls the load distribution of the cold water unit and the ice tank of the ice storage air-conditioning system according to the optimization result. And the ice storage air conditioning system can not only ensure the indoor environment quality, but also meet the running requirements of energy conservation and economy by virtue of multi-objective optimization control based on constraint conditions.

Description

Method and system for predicting cold load demand and distributing ice storage air conditioner load
Technical Field
The invention belongs to the field of air conditioner refrigeration, and particularly relates to a method and a system for cold load demand prediction and ice storage air conditioner load distribution.
Background
The energy problem is increasingly serious, the energy conservation and emission reduction work becomes the focus of all countries, and the air conditioner is one of the most common energy consumption devices in the building and accounts for about 60 percent of the total power and energy consumption. Especially during peak periods of air conditioner demand in summer, power failure and other grid faults are more common. The ice storage air conditioning system uses ethylene glycol aqueous solution as a secondary refrigerant, stores cold energy through an ice making mode and phase change latent heat, uses electricity to make ice for cold storage at a low-ebb electricity price period, uses a cold machine and ice melting combined cold supply at an electric power peak period, and realizes 'peak load shifting' and energy-saving operation work of air conditioning electricity.
The ice storage air conditioning system is a large-scale control system distributed in space and time, the initial investment is more, the current centralized architecture needs to complete networking configuration on all control nodes, a plurality of interconnected subsystems have more and scattered devices, and the workload of field configuration and system configuration is large. When the type of equipment or the target building changes, the controller needs to be programmed again case by case. In addition, as the system scale increases, problems such as link congestion and operation delay in data transmission frequently occur.
Disclosure of Invention
The invention aims to provide a method and a system for predicting cold load demand and distributing ice storage air conditioner load, which are used for solving the problems of high running energy consumption, running cost and energy consumption loss of an ice storage system in the prior art.
In order to realize the task, the invention adopts the following technical scheme:
the method for predicting the cold load demand of the target building comprises the following steps:
step a: dividing a target building into a plurality of space units by using a Mini-Max building space unit division method;
step b: collecting the data of the air conditioning boxes in each space unit, and obtaining the standard cooling supply load of each space unit according to the data of the air conditioning boxes;
step c: collecting cold load influence factors of the current time of a day to be regulated, respectively inputting the cold load influence factors of the current time and the standard supply cold load of each space unit into a PSO-BP network prediction model, and outputting a cold load demand prediction value of each space unit at the current time;
step d: if the current moment is not the last moment of the day to be regulated, the next moment of the day to be regulated is equal to the current moment, and the step c is returned to be executed, so that the cold load demand predicted value of each space unit at the next moment is obtained; and if the current moment is the last moment of the day to be regulated and controlled, stopping circulation, obtaining the cold load demand predicted value of each space unit at all moments, and obtaining the total cold load demand predicted value of the day to be regulated and controlled of the target building by using a spanning tree addition method.
Further, in the step b, the standard cooling load of each space unit is calculated by formula I according to the air conditioner box data,
Q'demand＝cwmw(ts-ti) Formula I
Wherein, Q'demandIs the standard cooling load of a space unit, and has the unit of kW, cwIs the specific heat capacity value of water, and the unit is J/(kg DEG C) mwThe mass of chilled water passing through the air conditioning cabinet is in kg, tsIs the inlet water temperature of the air conditioning box, and the unit is DEG CiThe temperature of the outlet water of the air conditioning box is shown in the unit of ℃.
Further, the cooling load influence factors at the current moment include: the outdoor air temperature at the current moment, the solar radiation intensity at the current moment, the relative humidity at the current moment, the outdoor air temperature at the previous moment, the solar radiation intensity at the previous moment, the air-conditioning cold load at the previous moment and the air-conditioning cold loads at the previous two moments.
The cold load demand forecasting system of the target building comprises a partitioning module, an acquisition module and a cold load demand forecasting module;
the partitioning module is used for partitioning a target building into a plurality of space units according to a Mini-Max building space unit partitioning method;
the acquisition module is used for acquiring the data of the air conditioning boxes in each space unit and the cold load influence factors at all times of the day to be regulated;
and the cold load demand prediction module obtains a total cold load demand prediction value of a day to be regulated by adopting a cold load demand prediction method.
The method comprises the steps of obtaining multiple groups of cold quantity distribution results by utilizing a distributed PSODE optimization algorithm according to a formula I, wherein the cold quantity distribution results comprise the value of the ice tank cold supply ratio and the values of the k cold machine partial load rates, and selecting any one group of cold quantity distribution results as the ice tank cold supply ratio in a target building and the k cold machine partial load rates to complete cold load distribution;
the cold quantity distribution result is less than or equal to the total cold load demand predicted value of the target building obtained by the cold load demand prediction method;
the cold quantity distribution result meets the requirement of minimum energy consumption, cost and energy consumption loss of a target building;
Figure BDA0002324044590000031
where t denotes the time sequence number, k denotes the refrigerator sequence number, PLRt(k) The partial load rate of the kth table cooler in the tth hour is represented, and the value range of the partial load rate is [0.3,1 ]]Tank denotes an ice bank, ηtank(t) represents [ B]And the cooling duty ratio of the ice groove in the middle tth hour is the ratio of the cooling capacity of the ice groove to the total cooling load demand of the target building, and the value range is [0,1 ]]。
Further, the condition that the cold quantity distribution result meets the minimum energy consumption, cost and energy consumption loss of the target building means that:
the three objective functions of the operation energy consumption objective function, the operation cost objective function and the energy consumption loss objective function are minimum:
wherein an energy consumption objective function f is operated1＝Wc+Wct+WpumpWherein W iscIs total energy consumption in the running period of the cooler, WctFor total energy consumption of cooling tower operating cycle, WpumpIs the total energy consumption in the pump operation period;
cost of operation objective function
Figure BDA0002324044590000041
Wherein e (t) is the electricity price per sampling step;
energy consumption loss objective function
Figure BDA0002324044590000042
Wherein delta is the cold quantity conversion rate of the cold machine ice storage in the ice storage stage, t1And t3Respectively the ice storage time and the cold supply time of the ice tank.
Further, the cold distribution result also needs to satisfy:
the refrigerating capacity of the refrigerator in each time period is less than or equal to the rated refrigerating capacity of the refrigerator;
the cold supply amount of the ice tank in the operation period is less than or equal to the total ice storage amount of the ice tank and more than or equal to 95 percent of the total ice storage amount;
the cold supply capacity of the ice tank at the current moment is less than or equal to the residual cold capacity of the ice tank at the current moment and the maximum cold supply capacity of the ice storage tank at the current moment.
An ice thermal storage air conditioning load distribution system, which comprises a processor, wherein the processor executes the ice thermal storage air conditioning load distribution method according to any one of claims 5 to 7 to set the cooling duty ratio of an ice groove in a target building and the partial load rate of k coolers, so as to complete the cooling load distribution.
Compared with the prior art, the invention has the following technical characteristics:
1. the method is based on the mathematical model of the ice storage air-conditioning system under the requirement of ensuring the comfort of the end user of the target building, takes the three aims of minimum system operation energy consumption, minimum operation cost and minimum energy consumption loss, optimizes the system based on the differential evolution improved particle group combination algorithm of the dispersion control structure, and controls the load distribution of the cold water unit and the ice tank of the ice storage air-conditioning system according to the optimization result. And the ice storage air conditioning system can not only ensure the indoor environment quality, but also meet the running requirements of energy conservation and economy by virtue of multi-objective optimization control based on constraint conditions.
2. The invention discloses a particle cluster integration algorithm (PSODE) with a distributed control structure and improved differential evolution, which consists of a series of calculation nodes (CPNs) written in an equipment model and a control algorithm, wherein the CPNs are connected through a network cable or wireless communication through the structure of actual engineering equipment. The distributed control structure saves on-site secondary development work and realizes a cooperative task in a self-organizing way.
Drawings
FIG. 1 is a diagram of a distributed control architecture of an ice storage air conditioning system;
FIG. 2 is a diagram comparing a centralized architecture and a distributed control architecture;
FIG. 3 is a flow chart of a distributed PSODE algorithm;
FIG. 4 is a schematic diagram of node communications;
FIG. 5 is a schematic diagram of optimal solution set distribution of a distributed PSODE algorithm and a conventional PSO algorithm;
FIG. 6 is a diagram illustrating an iterative process of a distributed PSODE algorithm and a conventional PSO algorithm;
FIG. 7 is a schematic of chiller/ice bank duty cycle;
FIG. 8 shows a cold mass distribution diagram.
Detailed Description
The terms disclosed in the present invention are explained below:
PSO-BP network prediction model: the model is a prediction model obtained according to "dynamic prediction model of cold load of ice storage air conditioner based on improved PSO-BP neural network" in volume 41, period 1, and month 2 of 2019 in the statement of civil engineering and environmental engineering (Chinese and English), and is used for inputting cold load influence factors and standard cold load and outputting predicted cold load of the air conditioner at a certain moment.
PSODE algorithm: the algorithm is a double-population Evolution strategy introducing an information exchange mechanism, one population is evolved according to a Particle Swarm Optimization (PSO) rule, the other population is evolved according to a Differential Evolution (DE) operation, and the two populations exchange information in the Evolution process to avoid that various populations are trapped in local Optimization.
The Mini-Max building space unit division method comprises the following steps: mini: the space range covered by the minimum terminal equipment which can be independently regulated and controlled by the same system equipment is referred to; max: the minimum unit coverage area of various devices of different systems is the maximum value. The minimum terminal equipment of various systems in the same space is found, the coverage area is calculated, and then the maximum value of the coverage area is taken as the division basis of the building space unit.
Spanning tree addition algorithm: and adding the results of the cold load prediction of each building space unit to obtain the total cold load demand of the target building. And initiating a cold load prediction result adding instruction by a computing unit, and establishing an undirected graph G (V, E) of the topological structure of the space unit nodes by adopting an automatic topological identification algorithm, wherein V represents all computing unit nodes in the undirected graph, and E represents a connecting line for connecting all computing unit nodes in the undirected graph. Establishing a minimum tree with the computing unit node as a root node, and adding the cold load prediction results to obtain a sum equivalent f, Qdemand＝f(x1,x2,...,xn)。
Inputting the cold load influence factor at the current moment and the standard supply cold load of the space unit into the cold load demand model of the space unit and training, and outputting the predicted value of the cold load demand of the space unit at the current moment;
example 1
The embodiment discloses a method for predicting the cold load demand of a target building, which comprises the following steps:
step a: dividing a target building into a plurality of space units by using a Mini-Max building space unit division method;
step b: collecting the data of the air conditioning boxes in each space unit, and obtaining the standard cooling supply load of each space unit according to the data of the air conditioning boxes;
step c: collecting cold load influence factors of the current time of a day to be regulated, respectively inputting the cold load influence factors of the current time and the standard supply cold load of each space unit into a PSO-BP network-based cold load demand model for each space unit, and outputting a cold load demand predicted value of each space unit at the current time;
step d: if the current moment is not the last moment of the day to be regulated, the next moment of the day to be regulated is equal to the current moment, and the step c is returned to be executed, so that the cold load demand predicted value of each space unit at the next moment is obtained; and if the current moment is the last moment of the day to be regulated and controlled, stopping circulation, obtaining the cold load demand predicted value of each space unit at all moments, and obtaining the total cold load demand predicted value of the day to be regulated and controlled of the target building by using a spanning tree addition method.
The embodiment also discloses a cold load demand forecasting system of the target building, which comprises a partitioning module, an acquisition module and a cold load demand forecasting module;
the partitioning module is used for partitioning a target building into a plurality of space units according to a Mini-Max building space unit partitioning method;
the acquisition module is used for acquiring the data of the air conditioning boxes in each space unit and the cold load influence factors at all times of the day to be regulated;
the cold load demand prediction module obtains a total cold load demand prediction value of a day to be regulated by adopting the inter-cooling load demand prediction method.
The embodiment also discloses a load distribution method of the ice storage air conditioner, which utilizes a distributed PSODE optimization algorithm to obtain a plurality of groups of cold quantity distribution results according to the formula I, wherein the cold quantity distribution results comprise the value of the cold supply ratio of the ice groove and the values of the partial load rates of k refrigerators, and any one group of cold quantity distribution results are selected as the cold supply ratio of the ice groove in the target building and the partial load rates of the k refrigerators to finish cold load distribution;
the cold distribution result is less than or equal to the total cold load demand predicted value of the target building obtained by the cold load demand prediction method according to any one of claims 1 to 3;
the cold quantity distribution result meets the requirement of minimum energy consumption, cost and energy consumption loss of a target building;
Figure BDA0002324044590000081
where t denotes the time sequence number, k denotes the refrigerator sequence number, PLRt(k) Denotes the tth hourThe value range of the partial load rate of the k-station cooler is [0.3, 1%]Tank denotes an ice bank, ηtank(t) represents [ B]And the cooling duty ratio of the ice groove in the middle tth hour is the ratio of the cooling capacity of the ice groove to the total cooling load demand of the target building, and the value range is [0,1 ]]。
Specifically, in the step b, the standard cooling load of each space unit is calculated according to the data of the air conditioning box through a formula I,
Q'demand＝cwmw(ts-ti) Formula I
Wherein, Q'demandIs the standard cooling load of a space unit, and has the unit of kW, cwIs the specific heat capacity value of water, and the unit is J/(kg DEG C) mwThe mass of chilled water passing through the air conditioning cabinet is in kg, tsIs the inlet water temperature of the air conditioning box, and the unit is DEG CiThe temperature of the outlet water of the air conditioning box is shown in the unit of ℃.
Specifically, the step c of obtaining the cold load influence factors includes the following substeps:
s301, the factors influencing the cold load requirement are more, the grey correlation degree analysis method is a multi-factor statistical analysis method, and the strength, the size and the sequence of the influence of the input factors on the output result are described by taking sample data of all the factors as the basis. If the input variable and the output result have basically the same change trend and speed, the correlation degree between the input variable and the output result is larger; otherwise, the correlation degree is smaller;
s302, establishing an original data matrix x of related indexesi；
xi＝(xi(0),xi(1),xi(2)...,xi(23)) (26)
S302, establishing an initialization matrix xi’；
Figure BDA0002324044590000082
S303, finding a difference sequence deltaoi(k)；
Figure BDA0002324044590000091
S304, calculating a correlation coefficient ξoi(k) Degree of correlation with grayoi；
Figure BDA0002324044590000092
Figure BDA0002324044590000093
Specifically, the cold load influencing factors at the current moment include: the outdoor air temperature at the current moment, the solar radiation intensity at the current moment, the relative humidity at the current moment, the outdoor air temperature at the previous moment, the solar radiation intensity at the previous moment, the air-conditioning cold load at the previous moment and the air-conditioning cold loads at the previous two moments.
In this embodiment, the electromechanical device is upgraded to an intelligent structure by embedding a computing node CPN (computing Process node), the CPNs are connected with each other to form a computing network, and adjacent CPNs cooperate with each other based on a communication protocol to realize a cooperation task in a self-organizing manner, thereby completing the optimal control of the working state setting of the refrigerator and the cooling duty ratio of the ice bank.
Specifically, the objective function established according to the energy consumption is obtained by the following steps:
s101, establishing an energy consumption model of primary side equipment provided by cold energy of an ice storage air conditioning system, wherein the energy consumption model comprises a cold machine, a cooling tower, a cooling pump and a solution pump;
a cold machine energy consumption model:
Figure BDA0002324044590000094
Figure BDA0002324044590000095
in the formula, COP (k) is the energy efficiency ratio of the kth stage cooler; PLR (k) is k cold part load ratios; a is1,a2,...a10Ten model coefficients of the refrigerator are obtained; t isCHWSSupplying chilled waterTemperature, deg.C; t isCWRThe return water temperature of cooling water is DEG C; wcThe total energy consumption in the operation period of the cooling machine is kW.h; t is sampling time in the operation period, and sampling is carried out once per hour, h; pc(t) is the running power of the refrigerator at the moment t, kW; k represents the number of coolers; qn(k) The rated power of the kth cold machine is kW.
Cooling tower energy consumption model:
Figure BDA0002324044590000101
Figure BDA0002324044590000102
in the formula (I), the compound is shown in the specification,
Figure BDA0002324044590000103
the load capacity of the cooling tower at t is kW; qcs(t) is the cooling capacity of the cooling machine at t hours, kW; wctThe total energy consumption of the cooling tower in the operation period is kW.h; wctAnd (t) is the energy consumption of the cooling tower t, kW & h, α represents a direct proportional coefficient, and the direct proportional coefficient is obtained by fitting according to actual engineering data, and other parameters have the same meanings as the parameters.
Pump energy consumption model:
Figure BDA0002324044590000104
Figure BDA0002324044590000105
Figure BDA0002324044590000106
in the formula, PCHWpump、PCWpumpAnd PEGSpumpThe power consumption of the freezing pump, the cooling pump and the solution pump is kW; rhowThe density of the chilled water and the cooling water is kg/m3；mCHW、mCWAnd mEGSIs the flow of chilled water, cooling water and ethyleneFlow rate of glycol solution, m3/h；HCHW、HCWAnd HEGSRepresenting a pressure difference, kPa, ηCHW、ηCWAnd ηEGSRepresenting the operating efficiency of these three types of pumps. The pressure difference and the pump operation efficiency are given by equations (8) to (14).
HCHW＝b0mCHW 2+b1wmCHW+b2w2(8)
ηCHW＝c0(mCHW/w)2+c1(mCHW/w)+c2(9)
HCW＝d0mCW 2+d1wmCW+d2w2(10)
ηCW＝e0(mCW/w)2+e1(mCW/w)+e2(11)
HEGS＝f0mEGS 2+f1wmEGS+f2w2(12)
ηEGS＝g0(mEGS/w)2+g1(mEGS/w)+g2(13)
Figure BDA0002324044590000111
The ratio of the pump rotational speed w is as in equation (14), n0The rated rotation speed of the pump, r/min; n is the actual rotating speed under the working condition, r/min; b0,b1,b2,....g0,g1,g2Parameters are obtained according to actual engineering fitting; other parameters have the same meanings as above. The working time of the cooling pump and the refrigerating pump is consistent with that of the refrigerator, namely the ice storage working condition and the refrigerating machine cooling working condition, and the energy consumption of the cooling pump and the refrigerating pump is shown in the formulas (15) and (16). The ethylene glycol solution pump operates under the ice storage working condition and the ice tank cooling working condition, and the energy consumption is as shown in formula (17).
Figure BDA0002324044590000112
Figure BDA0002324044590000113
Figure BDA0002324044590000114
In the formula, m, n and j respectively represent the number of a freezing pump, a cooling pump and a ethylene glycol solution pump; t is t1,t2,t3Respectively providing a calculation formula for ice storage time, cold machine working time and ice tank cold supply time h by an algorithm introduction module; other parameters have the same meanings as above.
And S102, establishing an objective function. The ice storage air conditioning system aims at solving the problems of energy utilization rate, power grid balancing, cost saving and the like. The running cost can be effectively reduced by increasing the ice storage amount of the off-peak electricity price at night, but the energy consumption loss is inevitably caused by multi-layer state conversion and heat dissipation in the ice storage process. The reduction of the ice storage amount is faced with the increase of the operation cost. Therefore, in order to improve the energy utilization efficiency and reduce the energy consumption loss and the operation cost of the system to the maximum extent, the objective function of minimum operation energy consumption, minimum operation cost and minimum energy consumption loss is adopted in the method, and the cooling strategy of each time duration in the operation period of the ice storage air conditioning system is optimized.
1) An energy consumption objective function is run.
f1＝WT＝Wc+Wct+Wpump(18)
2) An operating cost objective function. The running period operation cost of the ice storage air conditioning system is the sum of the products of the total energy consumption at each moment of the running period and the electricity price at the corresponding moment.
Figure BDA0002324044590000121
In the formula, Cost is the total operation Cost of the operation period of the ice storage air conditioning system; e (t) electricity price per sampling step; other parameters have the same meanings as above.
3) Energy consumption loss objective function. In the ice storage stage of the ice storage system, the cold quantity is provided by the cold machine for storing ice, the thermal resistance is increased along with the thickening of an ice layer in the ice storage process, the heat exchange is weakened, the ice storage capacity of the cold machine is reduced, and part of energy consumption loss is generated. Neglecting the influence of heat loss formed by heat exchange between the ice tank and air, the cold energy conversion rate of the cold machine ice storage in the ice storage stage is considered to be delta. Compared with the traditional air conditioner, the ice cold accumulation system increases the transmission and transfer of cold quantity, and the energy consumption generated by the solution pump in the ice accumulation stage and the ice tank cold supply stage is also used as part of energy consumption loss.
Figure BDA0002324044590000122
Specifically, the constraint condition established according to the total cold load demand predicted value of the target building includes:
1) the refrigerating capacity of the refrigerator in each time period is less than the rated refrigerating capacity of the refrigerator;
Q(k)＝Qn(k)·PLR(k)≤Qn(k) (21)
2) the total ice storage amount in the ice storage stage is smaller than the capacity of the ice tank, and in order to prevent the phenomenon of ice in ten thousand years, the cooling capacity of the ice tank in the operation period is smaller than the total ice storage amount of the ice tank and larger than 95 percent of the total ice storage amount;
Qice.st·0.95≤Qtank≤Qice.st(22)
3) the cold supply capacity of the ice tank at the current moment is smaller than the residual cold capacity of the ice tank at the current moment and smaller than the maximum cold supply capacity of the ice storage tank at the current moment;
Figure BDA0002324044590000123
4) in order to meet the requirement of indoor comfort level of a building and guarantee the saving of electric energy, the sum of the cooling capacity provided by the cold machine and the ice tank reaches the precision range meeting the requirement of cold load of the building.
Qc(t)+Qtank(t)-Qdemand(t)|≤ε·Qdemand(t) (24)
In the formula, QtankCooling capacity, kW, is supplied to the ice groove; qtank(t) the cooling capacity of the ice groove at t is kW; h is1,h2Fitting according to actual engineering data；Qdemand(t) the cold load demand at the end of the building at t, kW; ε is the range of accuracy required to meet the cooling load.
Setting ice storage time from night 00:00 to morning 08:00 for 8 hours in total, and cold supply time from 08:00 to 24:00 for 16 hours in total. At partial load rate PLR at kth cold stage tt(k) And t-time ratio η of cooling capacity of ice tank to current cooling load demandtank.c(t) is decision variable, 24 row vector groups α of the matrix A and 9 to 24 rows 16 row vector groups β of the matrix B form N-dimensional decision variable according to COP of the refrigerator and PLR change rule thereof, when PLR is 0.3 or less, refrigerating capacity of the refrigerator is small and energy consumption is high, therefore, upper and lower boundary values of the row vector group α are set to be 0.3 and 1, and the value range of the ice tank cooling proportion row vector group β is set to be [0,1]；
xi＝[α1,α2,...,α24,β9,...,β24](33)
Feasible solution set decision variable xiAnd calculating the running energy consumption, the running cost and the energy consumption loss of the ice storage system in the running period. The ten-item coefficient of the cold machine, the rated power of the pump, the rated rotating speed of the pump and other equipment parameters are provided by an equipment supplier, and the running parameters of the chilled water supply temperature, the cooling water return temperature, the density, the flow rate, the actual rotating speed of the pump and the like are obtained from actual engineering data. The ice storage time length, the cold supply time length of the cold machine and the cold supply time length of the ice tank are calculated by decision variables;
Figure BDA0002324044590000131
Figure BDA0002324044590000132
Figure BDA0002324044590000133
the embodiment discloses an ice storage air conditioner load distribution system which comprises k +1 nodes connected according to a topological structure, wherein each node is used for sequentially executing an optimization and adjustment task based on a distributed PSODE optimization algorithm according to the topological structure to control a corresponding refrigerator or ice tank, a feasible solution set is obtained after the optimization and adjustment task is executed for each node, and any one group in the feasible solution set is selected to set the cooling capacity of the ice tank in a target building and the refrigerating capacity of the k refrigerators
Specifically, for each node, whether the node iteration number meets the communication cycle needs to be judged, if yes, a feasible solution set and population information of the node are sent to a neighbor node according to the topological structure to serve as a basis for iteration in the next optimization and adjustment task, and if not, the node is continuously iterated until the iteration number meets the communication cycle.
Specifically, the communication cycle is 20 generations.
Specifically, the distributed PSODE optimization algorithm comprises the following substeps:
s1, initializing the speed and the position of the particles, and judging whether constraint conditions are met, if so, continuing the algorithm, and if not, regenerating;
s2, calculating the individual leader of the current particle according to the objective function;
s3, evaluating a particle adaptive value, and updating an external reserve set based on a Pareto domination relationship and a crowding degree distance;
wherein Pareto governs the relationships and strategies as follows:
Figure BDA0002324044590000141
if and only if:
Figure BDA0002324044590000142
and is
Figure BDA0002324044590000143
Is recorded as:
Figure BDA0002324044590000144
if the feasible solution is dominated by any feasible solution in the external reserve set, refusing to enter the external reserve set; the feasible set governs any one individual in the external reserve set, and all the governed individuals are deleted from the external reserve set; the feasible solution and all individuals in the external reserve set are independent of each other, and then the external reserve set can be entered.
And updating the external reserve set based on the crowdedness distance, and calculating the average distance between the feasible solution and the adjacent solution on each target to represent the crowdedness of the solution and other solutions, such as formulas (40) - (44). To ensure the diversity of the particles, the congestion distance values are arranged in ascending order, and feasible solutions of external reserves and capacity quantity are reserved.
Eim＝-[plimlog2(plim)+puimlog2(puim)](41)
Figure BDA0002324044590000151
cim＝dlim+duim(43)
Figure BDA0002324044590000152
S4, selecting a global leader of the particles based on a Gaussian sampling principle;
dividing the target space into a plurality of grids, determining the grids where feasible solutions in the external reserve sets are located, and counting the number of the feasible solutions in the external reserve sets contained in each grid. Randomly selecting a group of feasible solutions from the grids with the minimum number as mean values of Gaussian samples, wherein 1/2 of the distance between the two solutions which are positioned outside the grids of the feasible solutions and are closest to the feasible solutions is a variance, and the global guide is obtained through Gaussian sampling;
s5, dividing the population into two sub-populations, wherein one sub-population evolves according to a PSO rule, and the other sub-population evolves according to a DE rule;
s6, merging the populations, merging the external reserve sets, and updating the external reserve sets according to the strategy of S503;
s7, updating the individual leader and the global leader;
and S8, judging whether the current iteration number of the computing node meets the communication cycle. And if so, exchanging an optimization result with the neighbor node. Sending external storage set information and population information of the current optimization result to the neighbor nodes, comparing the received external storage set information and population information with the self optimization result by the neighbor nodes, reserving the result with better performance as the basis of the next evolution, and continuously forwarding the better result to the neighbor nodes; the other CPNs perform information interaction with the neighbor nodes according to the same rule; if not, repeating the steps S5-S7;
specifically, the communication cycle is set to 20 generations
And S9, judging whether the algorithm meets the iteration termination condition, and after the algorithm is terminated, reserving external reserve set information (namely a set corresponding to the cold quantity distribution result).
Specifically, after a population is segmented, the differential evolution algorithm is a population-based real parameter random optimization algorithm and comprises mutation, intersection and selection operations. Creating a variation individual for each target individual of the current population, generating a test individual by crossing the target individual with the variation individual, comparing the target individual with the test individual, selecting the individual with a better adaptive value as a next generation evolution basis, adopting a differential evolution algorithm consistent with a particle swarm algorithm, and adopting an external reserve set and crowding degree distance sorting strategy.
According to the particle swarm optimization algorithm with improved differential evolution, the particle swarm optimization algorithm has high convergence speed in the early stage of the optimization solution problem, and as the particles approach to the optimal particle position, the population is easy to lose diversity in the later stage of the evolution and is easy to fall into local optimization. When the particle swarm falls into the local optimal point, the optimal individual information of the differential evolution colony is absorbed to determine the position of the next generation, and the particles falling into the local optimal point are guided to deviate from the local optimal point and approach to the global optimal point with higher probability.
The ice cold storage air conditioning system has conflict among three optimization targets, does not have a unique optimal solution, and enables all target functions to obtain a specific set of Pareto optimal solutions. In order to improve the Pareto front end with better algorithm distributivity and extensibility, the multi-objective particle swarm optimization algorithm updates an external reserve set based on a crowded distance value and reserves non-inferior solutions.
In order to ensure the diversity of the particles, when the position and the speed of the particles are updated, the target space dimension is regarded as a plurality of grids, one particle is randomly selected from the grids containing the least non-inferior solutions as a mean value, the distance between two solutions closest to the particle is taken as a variance, the global leader of the particle is obtained by adopting Gaussian sampling, and the individual leader of each particle is calculated through an adaptive value.
Specifically, a PSO-BP neural network prediction model is adopted to predict the hourly cooling load demand of each space of the building, and the steps are as follows:
s401, initializing the scale of the particle swarm, wherein the scale comprises the number N of the particles of the swarm, the individual length D of the particles, the initial speed of the particles and the position of the particles. Calculating the individual length D of the particles as shown in formula (45);
D＝S1*S2+S2*S3+S2+S3(45)
in the formula, S1，S2，S3The number of BP neural network input layer factors, the number of hidden layer factors and the number of input layer factors are respectively;
and S402, calculating the particle fitness. Taking the sum of the absolute values of the errors of the predicted value and the observed value as a particle fitness value F, as shown in a formula (46);
Figure BDA0002324044590000171
wherein n is the number of samples, yiIs the true value of sample i, oiIs the predicted value of sample i.
And S403, comparing the particle fitness. The comparison rule is as follows:
if S isseLess than or equal to pbestfire, then pbestfire ═ Sse，pbest＝xi(ii) a Otherwise pbestfire and pbest are unchanged;
if S isseNot more than gbestfit, gbestfit ═ Sse，gbest＝xi(ii) a Otherwise, gbestfittess and gbest are unchanged;
wherein SseFor the current fitness value of the particle, pbestfit is the individual optimal fitness value of the particle, and gbestfit is the population global fitness valueThe optimal adaptive value is pbest which is the particle individual optimal value, gbest which is the population global optimal value, xiIs the current particle;
and S404, updating the position and the speed of the particles. Updating the particle velocity and position by using equations (47) and (48):
Figure BDA0002324044590000172
Figure BDA0002324044590000173
wherein P isi＝(Pi1，Pi2，...，PiD)、Vi＝(Vi1，Vi2，...，ViD)、Xi＝(Xi1，Xi2，...，XiD) Respectively d-dimensional extreme, velocity and position, P, of each generation of particlesg＝(Pg1，Pg2，...，PgD) For a global extreme value, randomly assigning values to the initial speed and the position by using a rand () function;
s405, current iteration times opoch and maximum iteration times DmaxBy comparison, if opoch > DmaxIf yes, stopping the algorithm, and if not, continuing the next iteration, wherein the current gbest is the weight and the threshold value of the BP neural network optimization;
s406, predicting the cold load prediction result of each building space unit according to the PSO-BP network prediction model, and obtaining the time-by-time cold load prediction result of the whole building according to a spanning tree addition method.
Example 2
On the basis of embodiment 1, the embodiment discloses a cold load demand prediction method and an ice storage air conditioner load distribution method. According to a parameter model of main equipment of the ice storage air-conditioning system, based on dynamic electricity price and equipment operation constraint conditions, the ice storage and ice melting scheduling of demand response is considered, and a power consumption model of a cold machine, a cooling tower and pump electromechanical equipment of the ice storage air-conditioning system is established by taking daily operation energy consumption, operation cost and energy consumption loss as optimization targets. Based on a Decentralized control architecture without a center, a distributed Differential Evolution Improved Multi-target particle Swarm Optimization algorithm (D-MOPSODE) is provided, and the time-by-time load rate of a cold machine and the time-by-time cooling proportion of an ice groove are solved. The algorithm is a double population evolution strategy introducing an information exchange mechanism, a cold machine and an ice groove of an embedded Computing Node (CPN) are used as Computing units, the CPNs are connected with each other to form a Computing network, and adjacent CPNs are mutually cooperated based on a communication protocol. According to the cold load prediction result of the ice storage air conditioning system, a certain CPN initiates an optimization regulation task and sends the task to the neighbor node, and the neighbor node receives the regulation task and continues to send the task until all nodes receive the task. And when the communication period is met, the calculation node and the neighbor node interact optimization variables, the neighbor node compares the optimization variables with the self optimization result, and the better result is reserved as the next generation evolution basis and is sent to the neighbor node. And repeating the steps until the solving requirement is met, and finishing the optimal control of the working state of the cold machine and the cold supply ratio of the ice tank by the whole computing network in a self-organizing way to finish the load distribution.
In the embodiment, a certain western-style security market is taken as a model verification experiment environment, the ice storage air-conditioning system of the market uses 3 double-working-condition centrifugal water chilling units, the rated power is 4430kW, ten model coefficients of the chilling unit are given in table 1, the total ice storage amount of a U-shaped internal ice-melting coil pipe is 73000kW, the U-shaped internal ice-melting coil pipe is designed according to 30% of the chilling load requirement of a typical design day, each of a freezing pump, a cooling pump and a solution pump is 3 devices with the same specification and model, the rated power is 160kW, the rated rotation speed is 1450r/min, the direct proportion coefficient α of the power consumption of a cooling tower and the0,b1,b2,....g0,g1,g2Ice storage rate delta of refrigerating capacity of refrigerator under ice storage condition, ice melting and cooling parameter h1,h2And the accuracy range epsilon of the market for cooling to meet the cooling load requirement is obtained by fitting according to the historical data of the market energy consumption acquisition system, and the detailed model parameters are shown in table 1. At present, the ice storage system in the market adopts a component storage mode and a fixed-proportion control strategy, and each sampling step size ice tank and the cold machine bear the cold load requirement together in proportion. Table 2 shows the time-of-use electricity prices of Xian City。
TABLE 1 model parameters
Figure BDA0002324044590000191
TABLE 2 time-of-use electricity price table of Xian city
Figure BDA0002324044590000192
Figure BDA0002324044590000201
And (3) adopting a grey correlation degree analysis method, and carrying out statistical analysis on the strength, the size and the sequence of the influence of each factor on the cold load result according to sample data of factors influencing the cold load data at the current moment, such as the outdoor air temperature at the current moment, the solar radiation intensity at the current moment, the outdoor wind speed at the current moment, the relative humidity at the current moment, the outdoor air temperature at the previous moment, the cold load at the previous moment and the like. The grey correlation degree of each influence factor and the air conditioner cooling load at the time T is shown in a table 3;
TABLE 3 Grey correlation between various influence factors and air conditioner cooling load at T moment
Figure BDA0002324044590000202
According to relevant parameters of the ice storage air-conditioning system in the market, hourly cooling load prediction data of 12 days in 7 months in 2017 are selected for carrying out night ice storage optimization, and the load distribution optimization of the next-day refrigerator and the ice tank is carried out. After the iteration of the algorithm is terminated, a group of solutions with the minimum crowding distance degree is selected from the Pareto optimal solution set, fig. 5 is a Pareto optimal solution set distribution diagram of the optimization algorithm, and fig. 7 is a proportion of a partial load rate of each cold machine and a cooling capacity of an ice groove in cooling time to a cooling load demand at the current moment in an operation period. The total ice storage amount is calculated according to an ice storage air conditioning system mathematical model and is 68875kw, which is about 35% of the cold load demand of the next day of the building, and the total cold supply of the ice tank is 66882.75kw, which reaches 97.11% of the total ice storage amount and meets the design requirement. The hourly part load rate of the cold machine fluctuates at the 0.85 position of the COP highest point, and the operation efficiency is high. Fig. 8 shows the distribution of the ice storage amount in the night valley period of each sampling step length and the cold amounts of the refrigerator and the ice tank in the cold supply stage, wherein the errors of the total cold supply amount and the required amount of the ice tank and the refrigerating unit in the cold supply stage are in an ideal state.

Claims (8)

1. The method for predicting the cold load demand of the target building is characterized by comprising the following steps of:
step a: dividing a target building into a plurality of space units by using a Mini-Max building space unit division method;
step b: collecting the data of the air conditioning boxes in each space unit, and obtaining the standard cooling supply load of each space unit according to the data of the air conditioning boxes;
step c: collecting cold load influence factors of the current time of a day to be regulated, respectively inputting the cold load influence factors of the current time and the standard supply cold load of each space unit into a PSO-BP network prediction model, and outputting a cold load demand prediction value of each space unit at the current time;
step d: if the current moment is not the last moment of the day to be regulated, the next moment of the day to be regulated is equal to the current moment, and the step c is returned to be executed, so that the cold load demand predicted value of each space unit at the next moment is obtained; and if the current moment is the last moment of the day to be regulated and controlled, stopping circulation, obtaining the cold load demand predicted value of each space unit at all moments, and obtaining the total cold load demand predicted value of the day to be regulated and controlled of the target building by using a spanning tree addition method.
2. The method for predicting a cooling demand of a target building as set forth in claim 1, wherein the standard cooling demand of each space unit is calculated by formula I based on the air-conditioning box data in step b,
Q'demand＝cwmw(ts-ti) Formula I
Wherein, Q'demandIs the standard cooling load of a space unit, and has the unit of kW, cwIs the specific heat capacity value of water, and the unit is J/(kg DEG C) mwThe mass of chilled water passing through the air conditioning cabinet is in kg, tsIs the inlet water temperature of the air conditioning box, and the unit is DEG CiThe temperature of the outlet water of the air conditioning box is shown in the unit of ℃.
3. The method of predicting a cooling demand of a target building as set forth in claim 1, wherein the current time cooling load influence factor includes: the outdoor air temperature at the current moment, the solar radiation intensity at the current moment, the relative humidity at the current moment, the outdoor air temperature at the previous moment, the solar radiation intensity at the previous moment, the air-conditioning cold load at the previous moment and the air-conditioning cold loads at the previous two moments.
4. The system for predicting the cold load demand of the target building is characterized by comprising a partitioning module, an acquisition module and a cold load demand prediction module;
the partitioning module is used for partitioning a target building into a plurality of space units according to a Mini-Max building space unit partitioning method;
the acquisition module is used for acquiring the data of the air conditioning boxes in each space unit and the cold load influence factors at all times of the day to be regulated;
the cold load demand forecasting module adopts any one of the cold load demand forecasting methods in claims 1 to 3 to obtain a total cold load demand forecasting value of a day to be regulated.
5. The ice storage air conditioner load distribution method is characterized in that a distributed PSODE optimization algorithm is utilized to obtain a plurality of groups of cold distribution results according to a formula I, the cold distribution results comprise values of ice groove cold supply ratio and values of k cold machine partial load rates, and any one group of cold distribution results are selected as the cold supply ratio of an ice groove in a target building and the k cold machine partial load rates to complete cold load distribution;
the cold distribution result is less than or equal to the total cold load demand predicted value of the target building obtained by the cold load demand prediction method according to any one of claims 1 to 3;
the cold quantity distribution result meets the requirement of minimum energy consumption, cost and energy consumption loss of a target building;
Figure FDA0002324044580000021
where t denotes the time sequence number, k denotes the refrigerator sequence number, PLRt(k) The partial load rate of the kth table cooler in the tth hour is represented, and the value range of the partial load rate is [0.3,1 ]]Tank denotes an ice bank, ηtank(t) represents [ B]And the cooling duty ratio of the ice groove in the middle tth hour is the ratio of the cooling capacity of the ice groove to the total cooling load demand of the target building, and the value range is [0,1 ]]。
6. The ice storage air conditioning load distribution method of claim 5, wherein the cold distribution result meeting the minimum energy consumption, cost and energy loss of the target building is:
the three objective functions of the operation energy consumption objective function, the operation cost objective function and the energy consumption loss objective function are minimum:
wherein an energy consumption objective function f is operated1＝Wc+Wct+WpumpWherein W iscIs total energy consumption in the running period of the cooler, WctFor total energy consumption of cooling tower operating cycle, WpumpIs the total energy consumption in the pump operation period;
cost of operation objective function
Figure FDA0002324044580000031
Wherein e (t) is the electricity price per sampling step;
energy consumption loss objective function
Figure FDA0002324044580000032
Wherein delta is the cold quantity conversion rate of the cold machine ice storage in the ice storage stage, t1And t3Respectively the ice storage time and the cold supply time of the ice tank.
7. An ice storage air conditioning load distribution method as claimed in claim 5, wherein the cold distribution result also needs to satisfy:
the refrigerating capacity of the refrigerator in each time period is less than or equal to the rated refrigerating capacity of the refrigerator;
the cold supply amount of the ice tank in the operation period is less than or equal to the total ice storage amount of the ice tank and more than or equal to 95 percent of the total ice storage amount;
the cold supply capacity of the ice tank at the current moment is less than or equal to the residual cold capacity of the ice tank at the current moment and the maximum cold supply capacity of the ice storage tank at the current moment.
8. An ice thermal storage air conditioning load distribution system, characterized in that the system comprises a processor, the processor executes the ice thermal storage air conditioning load distribution method according to any one of claims 5-7 to set the cooling duty ratio of the ice slot in the target building and the partial load rate of k coolers, and the cooling load distribution is completed.
CN201911309184.9A 2019-12-18 2019-12-18 Method and system for predicting cold load demand and distributing ice storage air conditioner load Active CN111076376B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911309184.9A CN111076376B (en) 2019-12-18 2019-12-18 Method and system for predicting cold load demand and distributing ice storage air conditioner load

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911309184.9A CN111076376B (en) 2019-12-18 2019-12-18 Method and system for predicting cold load demand and distributing ice storage air conditioner load

Publications (2)

Publication Number Publication Date
CN111076376A true CN111076376A (en) 2020-04-28
CN111076376B CN111076376B (en) 2021-03-16

Family

ID=70315420

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911309184.9A Active CN111076376B (en) 2019-12-18 2019-12-18 Method and system for predicting cold load demand and distributing ice storage air conditioner load

Country Status (1)

Country Link
CN (1) CN111076376B (en)

Cited By (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111561732A (en) * 2020-05-18 2020-08-21 瑞纳智能设备股份有限公司 Heat exchange station heat supply adjusting method and system based on artificial intelligence
CN111623491A (en) * 2020-06-11 2020-09-04 西安建筑科技大学 Variable speed water pump operation adjusting method based on collaborative optimization strategy
CN111706962A (en) * 2020-05-25 2020-09-25 珠海格力电器股份有限公司 Control device and method of multi-air-conditioning system and multi-air-conditioning system
CN112161352A (en) * 2020-09-29 2021-01-01 上海电力大学 Multi-objective optimization operation method considering cost and energy consumption for ice storage system
CN112413823A (en) * 2020-10-15 2021-02-26 南京淳宁电力科技有限公司 Distributed energy optimization management method of central air conditioning system in demand response mode
CN112465230A (en) * 2020-11-30 2021-03-09 施耐德电气（中国）有限公司 Self-adaptive water chilling unit starting combination optimization prediction method and system
CN112696750A (en) * 2021-01-22 2021-04-23 德莱创节能科技（苏州）有限公司 Cold accumulation control method and device for air conditioning device with cold accumulation function
CN112966431A (en) * 2021-02-04 2021-06-15 西安交通大学 Data center energy consumption joint optimization method, system, medium and equipment
CN113048549A (en) * 2021-03-17 2021-06-29 瑞纳智能设备股份有限公司 Heating circulating pump adjusting method based on artificial intelligence
CN113258584A (en) * 2021-06-18 2021-08-13 中国电力科学研究院有限公司 Multi-element load tracking and adjusting device and method for sensing real-time running state of electric power
CN113325762A (en) * 2021-05-25 2021-08-31 西安交通大学 Intelligent building personalized energy utilization control method, system, device and equipment
CN113435665A (en) * 2021-07-19 2021-09-24 西安建筑科技大学 Function partition-oriented shopping mall cold load prediction method and system
CN114234401A (en) * 2021-12-27 2022-03-25 上海美控智慧建筑有限公司 Control method and device of ice storage air conditioning system and electronic equipment
CN114251753A (en) * 2021-12-29 2022-03-29 西安建筑科技大学 Ice storage air conditioner cold load demand prediction distribution method and system
CN114322209A (en) * 2021-12-27 2022-04-12 上海美控智慧建筑有限公司 Data processing method and device of ice storage air conditioning system and electronic equipment
CN114326987A (en) * 2021-10-08 2022-04-12 腾讯科技（深圳）有限公司 Refrigeration system control and model training method, device, equipment and storage medium
CN114636212A (en) * 2022-04-22 2022-06-17 苏州思萃融合基建技术研究所有限公司 GRNN-based multi-chiller system operation control method
CN114857743A (en) * 2022-01-25 2022-08-05 西安建筑科技大学 Terminal valve optimization control method and system based on market partition load prediction

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106152343A (en) * 2016-07-05 2016-11-23 西安建筑科技大学 A kind of ice-chilling air conditioning system design optimization method based on Life cycle
CN205980188U (en) * 2016-08-05 2017-02-22 上海冰核时代科技中心（有限合伙） Ice cold -storage optimal control system based on load forecast
CN106958913A (en) * 2017-02-16 2017-07-18 中节能城市节能研究院有限公司 A kind of weather compensation type building air-conditioner energy-storage system
CN107120764A (en) * 2017-06-20 2017-09-01 天津城建大学 The optimization method of ice-chilling air conditioning system and its control method
US20190195526A1 (en) * 2015-10-08 2019-06-27 Johnson Controls Technology Company Building control systems with optimization of equipment life cycle economic value while participating in ibdr and pbdr programs
CN109974231A (en) * 2019-03-19 2019-07-05 南京南瑞继保电气有限公司 Ice-storage air-conditioning ice gauge draws method, apparatus, equipment and medium

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190195526A1 (en) * 2015-10-08 2019-06-27 Johnson Controls Technology Company Building control systems with optimization of equipment life cycle economic value while participating in ibdr and pbdr programs
CN106152343A (en) * 2016-07-05 2016-11-23 西安建筑科技大学 A kind of ice-chilling air conditioning system design optimization method based on Life cycle
CN205980188U (en) * 2016-08-05 2017-02-22 上海冰核时代科技中心（有限合伙） Ice cold -storage optimal control system based on load forecast
CN106958913A (en) * 2017-02-16 2017-07-18 中节能城市节能研究院有限公司 A kind of weather compensation type building air-conditioner energy-storage system
CN107120764A (en) * 2017-06-20 2017-09-01 天津城建大学 The optimization method of ice-chilling air conditioning system and its control method
CN109974231A (en) * 2019-03-19 2019-07-05 南京南瑞继保电气有限公司 Ice-storage air-conditioning ice gauge draws method, apparatus, equipment and medium

Cited By (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111561732A (en) * 2020-05-18 2020-08-21 瑞纳智能设备股份有限公司 Heat exchange station heat supply adjusting method and system based on artificial intelligence
CN111706962B (en) * 2020-05-25 2021-05-18 珠海格力电器股份有限公司 Control device and method of multi-air-conditioning system and multi-air-conditioning system
CN111706962A (en) * 2020-05-25 2020-09-25 珠海格力电器股份有限公司 Control device and method of multi-air-conditioning system and multi-air-conditioning system
CN111623491A (en) * 2020-06-11 2020-09-04 西安建筑科技大学 Variable speed water pump operation adjusting method based on collaborative optimization strategy
CN112161352A (en) * 2020-09-29 2021-01-01 上海电力大学 Multi-objective optimization operation method considering cost and energy consumption for ice storage system
CN112413823A (en) * 2020-10-15 2021-02-26 南京淳宁电力科技有限公司 Distributed energy optimization management method of central air conditioning system in demand response mode
CN112465230A (en) * 2020-11-30 2021-03-09 施耐德电气（中国）有限公司 Self-adaptive water chilling unit starting combination optimization prediction method and system
CN112696750A (en) * 2021-01-22 2021-04-23 德莱创节能科技（苏州）有限公司 Cold accumulation control method and device for air conditioning device with cold accumulation function
CN112966431A (en) * 2021-02-04 2021-06-15 西安交通大学 Data center energy consumption joint optimization method, system, medium and equipment
CN112966431B (en) * 2021-02-04 2023-04-28 西安交通大学 Data center energy consumption joint optimization method, system, medium and equipment
CN113048549A (en) * 2021-03-17 2021-06-29 瑞纳智能设备股份有限公司 Heating circulating pump adjusting method based on artificial intelligence
CN113325762B (en) * 2021-05-25 2023-04-28 西安交通大学 Intelligent building personalized energy consumption control method, system, device and equipment
CN113325762A (en) * 2021-05-25 2021-08-31 西安交通大学 Intelligent building personalized energy utilization control method, system, device and equipment
CN113258584A (en) * 2021-06-18 2021-08-13 中国电力科学研究院有限公司 Multi-element load tracking and adjusting device and method for sensing real-time running state of electric power
CN113435665A (en) * 2021-07-19 2021-09-24 西安建筑科技大学 Function partition-oriented shopping mall cold load prediction method and system
CN114326987A (en) * 2021-10-08 2022-04-12 腾讯科技（深圳）有限公司 Refrigeration system control and model training method, device, equipment and storage medium
CN114326987B (en) * 2021-10-08 2023-10-20 腾讯科技（深圳）有限公司 Refrigerating system control and model training method, device, equipment and storage medium
CN114322209A (en) * 2021-12-27 2022-04-12 上海美控智慧建筑有限公司 Data processing method and device of ice storage air conditioning system and electronic equipment
CN114234401A (en) * 2021-12-27 2022-03-25 上海美控智慧建筑有限公司 Control method and device of ice storage air conditioning system and electronic equipment
CN114251753A (en) * 2021-12-29 2022-03-29 西安建筑科技大学 Ice storage air conditioner cold load demand prediction distribution method and system
CN114857743A (en) * 2022-01-25 2022-08-05 西安建筑科技大学 Terminal valve optimization control method and system based on market partition load prediction
CN114636212A (en) * 2022-04-22 2022-06-17 苏州思萃融合基建技术研究所有限公司 GRNN-based multi-chiller system operation control method
CN114636212B (en) * 2022-04-22 2024-01-30 苏州思萃融合基建技术研究所有限公司 GRNN-based running control method for multiple water chilling unit systems

Also Published As

Publication number Publication date
CN111076376B (en) 2021-03-16

Similar Documents

Publication Publication Date Title
CN111076376B (en) Method and system for predicting cold load demand and distributing ice storage air conditioner load
CN101782258B (en) Energy-saving method for air conditioner
CN111191820B (en) Site selection and volume fixation optimization planning method for energy storage device in comprehensive energy system
US20160018125A1 (en) Energy management systems and methods of use
CN114383299B (en) Central air-conditioning system operation strategy optimization method based on big data and dynamic simulation
CN110107989A (en) Small-sized based on chilled water return water temperature optimum set point determines frequency water cooler and becomes temperature control method of water
US11610214B2 (en) Deep reinforcement learning based real-time scheduling of Energy Storage System (ESS) in commercial campus
CN113739365A (en) Central air-conditioning cold station group control energy-saving control method, device, equipment and storage medium
CN116398994B (en) Water chilling unit group control optimization method based on load prediction
CN109974231A (en) Ice-storage air-conditioning ice gauge draws method, apparatus, equipment and medium
KR20180138371A (en) Method for evaluating data based models and conducting predictive control of capsule type ice thermal storage system using the same
CN111737884A (en) Multi-target random planning method for micro-energy network containing multiple clean energy sources
CN112413823A (en) Distributed energy optimization management method of central air conditioning system in demand response mode
CN115882463A (en) Commercial building air conditioner load schedulable potential evaluation method
Zhuang et al. A decentralized method for energy conservation of an HVAC system
CN114282729A (en) Load prediction-based ice storage air conditioner optimal scheduling method
Dai et al. Multi-agent based distributed cooperative control of air-conditioning systems for building fast demand response
CN111401658A (en) Parallel cold load prediction method based on building space unit
CN114251753A (en) Ice storage air conditioner cold load demand prediction distribution method and system
CN114936529A (en) Temperature control load group aggregation model, modeling method and temperature control load group adjustable potential evaluation method
CN111473480A (en) Central air conditioner energy-saving control method based on decision tree classification
Hussein et al. Energy saving by reinforcement learning for multi-chillers of HVAC systems
CN111339474B (en) Comprehensive energy system prediction operation method based on trend prediction analysis method
Tuchowski et al. Optimizing the air source heat pump's working time in the context of increasing the energy efficiency ratio
CN112432269A (en) Method and system for optimizing set value of pressure difference of refrigerating water pump of refrigerating room

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant